# Copyright 2020 The HuggingFace Datasets Authors and Santiago Hincapie Potes.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TODO: Add a description here."""


import csv
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{yu2019lytnet,
  title = {LYTNet: A Convolutional Neural Network for Real-Time Pedestrian Traffic Lights and Zebra Crossing Recognition for the Visually Impaired},
  author = {Yu, Samuel and Lee, Heon and Kim, John},
  booktitle = {Computer Analysis of Images and Patterns (CAIP)},
  month = {Aug},
  year = {2019}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://github.com/samuelyu2002/ImVisible"

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLS = {
    "imgs": "ptl_dataset.tar.gz",
    "train": "training_file.csv",
    "validation": "validation_file.csv",
    "test": "testing_file.csv",
}


# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class ImVision(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    VERSION = datasets.Version("1.1.0")

    def _info(self):
        features = datasets.Features(
            {
                "img": datasets.Image(),
                "boxes": datasets.features.Sequence({
                    "label": datasets.Value("int8"),                    
                    "occluded": datasets.Value("bool"),
                    "x_max": datasets.Value("float"),
                    "x_min": datasets.Value("float"),
                    "y_max": datasets.Value("float"),
                    "y_min": datasets.Value("float"),
                }),
            }
        )
    
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=features,  # Here we define them above because they are different between the two configurations
            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
            # supervised_keys=("sentence", "label"),
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        urls = _URLS
        data_dir = dl_manager.download_and_extract(urls)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "img_folder": os.path.join(data_dir["imgs"], "PTL_Dataset_876x657/"),
                    "labels": data_dir["train"],
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "img_folder": os.path.join(data_dir["imgs"], "PTL_Dataset_876x657/"),
                    "labels": data_dir["test"],
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "img_folder": os.path.join(data_dir["imgs"], "PTL_Dataset_876x657/"),
                    "labels": data_dir["validation"],
                },
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, img_folder, labels):
        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
        with open(labels, encoding="utf-8") as f:
            reader = csv.reader(f)
            for key, row in enumerate(reader):
                if key == 0:
                    continue

                fname, label, x_min, y_min, x_max, y_max, occluded = row
                yield key - 1, {
                    "img": os.path.join(img_folder, fname),
                    "boxes": [
                        {
                            "label": int(label),
                            "occluded": occluded != "not_blocked",
                            "x_max": float(x_max),
                            "x_min": float(x_min),
                            "y_max": float(y_max),
                            "y_min": float(y_min),
                        }
                    ]
                }